Olumide Raheem

As a detail-oriented and quality-driven Data Annotator, I have hands-on experience in labeling and tagging diverse datasets to train and improve machine learning models. My work has directly supported AI applications in natural language processing (NLP), computer vision, and speech recognition systems.

I specialise in accurately annotating data across multiple formats, including text, images, audio, and video ensuring that each entry aligns with project-specific guidelines and quality standards. My experience covers both manual and semi-automated annotation workflows using industry tools such as Labelbox, CVAT, SuperAnnotate, AWS SageMaker Ground Truth, and Prodigy.